RAG, Fine-Tuning & ContextMinki Kang et al.
ACON published
AIMade by AIWritten by AI (Claude) from the source below. Check anything important against the source.How it's made
ACON published — iterative compression guided by failure analysis cuts the tokens in long-horizon agents' observation and action histories by 26–54%
Why it matters
A direction that shrinks context without fine-tuning, so that even small models can be used as long-horizon agents
Source — Primary sources first; secondary reports are labelled “Press”
Paperarxiv.orghttps://arxiv.org/abs/2510.00615Added in Week 35, 2026 · See that week
On the map — ② RAG, Fine-Tuning line
- Previous stopContext editing and memory tool launch in beta on the Claude API
- This recordACON
- Next stopMishra et al. publish SoK: Agentic Retrieval-Augmented Generation